KSTAR¶
The “Conifg” class¶
The “KinaseActivity” class¶
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class
kstar.activity.kstar.KinaseActivity(evidence, logger, evidence_columns={'site': 'KSTAR_SITE', 'substrate': 'KSTAR_ACCESSION'})[source]¶ Methods
add_network(network_id, network[, network_size])Add network to be analyzed
aggregate_activities([activities])Aggregate network activity using median for all activites
Summarizes normalized kinase activity results of all networks by kinase Summaries provided : median original activity average original activity median normalized activity average normalized activity count significant fraction significant
calculate_fdr(activities[, default_alpha])Given raw kinase activities and the normalizaiton factors, FDR correction is performed on activities for all networks using normalizaiton factor as FDR alpha.
Perform hypergeometric kinase activity analysis given evidence on all networks
Hypergeometric Cumulative Distribution Function calculated for each kinase given evidence
calculate_kinase_activities([data_columns, …])Calculates combined activity of experiments based that uses a threshold value to determine if an experiment sees a site or not To use values use ‘mean’ as agg To use count use ‘count’ as agg - present if not na
calculate_normalized_activity(…[, …])Activity is normalized based on kinase normalization factors. Added Columns : Normalization Factor : normalization factor for given Kinase Significant : 1 if Kinase Activity <= Normalization Factor Normalized Activity : ( Kinase Activity ) / ( Normalization Factor ) * Normalization Multiplier.
complete_fdr_response(response, alpha)FDR correction performed on response dataframe by first melting all experimental columns before performing FDR correction through Two-stage Benjamini, Krieger, & Yekutieli FDR procedure
fdr_corrected_activity(kinase_activity, alpha)Performs False Discovery Rate correction using Two-stage Benjamini, Krieger, & Yekutieli FDR procedure Benjamini, Yoav, Abba M.
find_pvalue_limits(data_columns[, agg, …])For each data column and network find the lowest p-value achievable and how many seen sites are required to get to that limit. Assumptions - kinase size in network is same for all kinases.
normalize_activites([activities, …])Generates the normalized activites and corresponding summary statistics
run_kstar_analysis(log, networks[, …])A super method to establish a kstar KinaseActivity object from an experiment with an activity log add the networks, calculate, aggregate, and summarize the activities into a final activity object
save_kstar(name, odir)Having performed kinase activities (run_kstar_analyis), save each of the important dataframes to files and the final pickle Saves an activities, aggregated_activities, summarized_activities tab-separated files Saves a pickle file of dictionary
set_evidence(evidence[, columns])Evidence to use in analysis
set_network_columns(network_columns)sets the network columns to use in analysis Parameters ———- network_columns : dict required keys : substrate, site, kinase
set_normalizers(normalizers)Sets the Kinase normalization values to be used in calculating the updated p-values Updated p-values normalized via original p-value / normalization-factor * normalization_factor
summarize_activites([activities, method])Builds a single combined dataframe from the provided activites such that each piece of evidence is given a single column.
add_networks_batch
aggregate_fdr
experiment_fdr_response
set_data_columns
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add_network(network_id, network, network_size=None)[source]¶ Add network to be analyzed
- Parameters
- network_idstr
name of the network
- networkpandas DataFrame
network with columns substrate_id, site, kinase_id
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aggregate_activities(activities=None)[source]¶ Aggregate network activity using median for all activites
- activitiesdict
key : Experiment value : kinase activity result
- Returns
- summariesdict
key : experiment value : summarized kinase activities accross networks
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aggregate_normalized_activities(normalized_activities)[source]¶ Summarizes normalized kinase activity results of all networks by kinase Summaries provided :
median original activity average original activity median normalized activity average normalized activity count significant fraction significant
- Parameters
- normalized_activitypandas df
Normalized kinase activty
- Returns
- summarypandas df
summarized data of all networks by kinase
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calculate_fdr(activities, default_alpha=0.05)[source]¶ Given raw kinase activities and the normalizaiton factors, FDR correction is performed on activities for all networks using normalizaiton factor as FDR alpha.
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calculate_hypergeometric_activities(evidence, name)[source]¶ Perform hypergeometric kinase activity analysis given evidence on all networks
- Parameters
- evidencepandas df
subset of class evidence variable where data is filtered based on experiment
- namestr
name of experiment being performed
- Returns
- fdr_actpd DataFrame
network : network name, from networks key frequency : number of times kinase was seen in subgraph of evidence and network kinase_activity : hypergeometric kinase activity fdr_corrected_kinase_activity : kinase activity after fdr correction significant : whether kinase activity is significant based on fdr alpha
- combinedpd DataFrame
significant : number of networks where kinase was found to be significant fraction_significant : fraction of networks kinase was found to be significant through FDR correction avg_p_value : combined p-values of kinase using mean median_p_value : combined p-values of kinase using median
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calculate_hypergeometric_single_network(evidence, network_id)[source]¶ - Hypergeometric Cumulative Distribution Function calculated for each kinase given evidence
k : number of times kinase seen in evidence M : number of unique sites in network n : number of times kinase seen in network N : size of evidence
- Parameters
- evidencepandas df
subset of kstar evidence that has been filtered to only include evidence associated with experimetn
- network_idstr
network to use for analysis
- Returns
- resultspandas df
Hypergeometric results of evidence for given network index : kinase_id columns
frequency : number of times kinase seen in network kinase_activity : activity derived from hypergometric cdf
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calculate_kinase_activities(data_columns=None, agg='count', threshold=0.0, greater=True)[source]¶ Calculates combined activity of experiments based that uses a threshold value to determine if an experiment sees a site or not To use values use ‘mean’ as agg To use count use ‘count’ as agg - present if not na
- Parameters
- data_columnslist
columns that represent experimental result, if None, takes the data: columns of the experiment. Pass this value in, if seeking to calculate on fewere than all available data columns
- thresholdfloat
threshold value used to filter rows
- aggstring
method to use when aggregating duplicate substrate-sites
- Returns
- activitiesdict
key : experiment value : pd DataFrame
network : network name, from networks key kinase : kinase examined frequency : number of times kinase was seen in subgraph of evidence and network kinase_activity : hypergeometric kinase activity
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calculate_normalized_activity(kinase_activity, normalizers, default_normalization=0.05, normalization_multiplier=0.05)[source]¶ Activity is normalized based on kinase normalization factors. Added Columns :
Normalization Factor : normalization factor for given Kinase Significant : 1 if Kinase Activity <= Normalization Factor Normalized Activity : ( Kinase Activity ) / ( Normalization Factor ) * Normalization Multiplier
- Parameters
- kinase_activitypandas df
hypergeometric activity calculated through KSTAR algorithm
- default_normalizationfloat
Normalization factor to use if one not provided
- normalization_multiplierfloat
normalization multiplier to use for calculated normalized kinase activity
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complete_fdr_response(response, alpha)[source]¶ FDR correction performed on response dataframe by first melting all experimental columns before performing FDR correction through Two-stage Benjamini, Krieger, & Yekutieli FDR procedure
- Parameters
- responsepandas df
- alphafloat
fdr alpha to use for significance
- Returns
- fdrpandas df
fdr corrected values
- significantpandas df
binary significant/not based on FDR
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fdr_corrected_activity(kinase_activity, alpha)[source]¶ Performs False Discovery Rate correction using Two-stage Benjamini, Krieger, & Yekutieli FDR procedure Benjamini, Yoav, Abba M. Krieger, and Daniel Yekutieli. 2006. “Adaptive Linear Step-up Procedures That Control the False Discovery Rate.” Biometrika 93 (3) (September 1): 491–507. doi:10.1093/biomet/93.3.491.
- Parameters
- kinase_activitypandas df
kinase activity p-values
- alphaflaot
FWER, family-wise error rate
- Returns
- resultspandas df
- kinase_activity dataframe with added columns
significant : 1 if p-value is statistically significant given alpha fdr_corrected_kinase_activity : fdr corrected activity by tsbky method
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find_pvalue_limits(data_columns, agg='count', threshold=0.0)[source]¶ For each data column and network find the lowest p-value achievable and how many seen sites are required to get to that limit. Assumptions
kinase size in network is same for all kinases
- Parameters
- data_columnslist
what columns in evidence to compare
- aggstr
- aggregate function - what function to use for determining if site is present
count : use when using activity_count mean : use when using activity_threshold
- thresholdfloat
threshold to use in determining if site present in evidence
- Returns
- all_limitspandas DataFrame
p-value limits of each column for each network columns:
evidence evidence data column network network being compared kinase kinase being evaluated evidence_size size of evidence limit_size number of sites to get non-zero p-value p-value p-value generated
- limit_summarypandas DataFrame
summary of all_limits by taking average over by evidence
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normalize_activites(activities=None, default_normalization=0.05, normalization_multiplier=0.05)[source]¶ Generates the normalized activites and corresponding summary statistics
- Parameters
- activitesdict
hypergeometric activites generated by KSTAR algorithm key : experiment value : hypergeometric result
- Returns
- normalized_activitiesdict
normalized Kinase Activity results key : experiment value : normalized results
- summarized_activitesdict
summary statistics of normalized kinase activity key : experiment value : summary statistics
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run_kstar_analysis(log, networks, phosphoTypes=['Y', 'ST'], data_columns=None, agg='count', threshold=0.0, greater=True)[source]¶ A super method to establish a kstar KinaseActivity object from an experiment with an activity log add the networks, calculate, aggregate, and summarize the activities into a final activity object
- Parameters
- experiment: pandas df
experiment dataframe that has been mapped, includes KSTAR_SITE, KSTAR_ACCESSION, etc.
- log: string
File name to write activity log error and update to
- networks: dictionary of dictionaries
Outer dictionary keys are ‘Y’ and ‘ST’. Establish a network by loading a pickle of desired networks. See the helpers and config file for this. If downloaded from FigShare, then the GLOBAL network pickles in config file can be loaded For example: networks[‘Y’] = pickle.load(open(config.NETWORK_Y_PICKLE, “rb” ))
- phosphoTypes: {[‘Y’, ‘ST’], [‘Y’], [‘ST’]}
Which substrate/kinaset-type to run activity for: Both [‘Y, ‘ST’] (default), Tyrosine [‘Y’], or Serine/Threonine [‘ST’]
- data_columnslist
columns that represent experimental result, if None, takes the columns that start with `data:’’ in experiment. Pass this value in as a list, if seeking to calculate on fewer than all available data columns
- agg{‘count’, ‘mean’}
method to use when aggregating duplicate substrate-sites. Count combines multiple representations and adds if values are non-NaN ‘mean’ uses the mean value of numerical data from multiple representations of the same peptide
- thresholdfloat
threshold value used to filter rows
- greater: Boolean
whether to keep sites that have a numerical value >=threshold (TRUE, default) or <=threshold (FALSE)
- Returns
- kinactDict: dictionary of Kinase Activity Objects
Outer keys are phosphoTypes run ‘Y’ and ‘ST’ Includes the activities dictionary (see calculate_kinase_activities) aggregation of activities across networks (see aggregate activities) activity summary (see summarize_activities)
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save_kstar(name, odir)[source]¶ Having performed kinase activities (run_kstar_analyis), save each of the important dataframes to files and the final pickle Saves an activities, aggregated_activities, summarized_activities tab-separated files Saves a pickle file of dictionary
- Parameters
- kinactDict: dictionary of Kinase Activity Objects
Outer keys are phosphoTypes run ‘Y’ and ‘ST’ Includes the activities dictionary (see calculate_kinase_activities) aggregation of activities across networks (see aggregate activities) activity summary (see summarize_activities)
- name: string
The name to use when saving activities
- odir: string
Outputdirectory to save files and pickle to
- Returns
- Nothing
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set_evidence(evidence, columns={'site': 'KSTAR_SITE', 'substrate': 'KSTAR_ACCESSION'})[source]¶ Evidence to use in analysis
- Parameters
- evidencepandas DataFrame
substrate sites with activity seen. columns : dict for column mapping
substrate : Uniprot ID (P12345) site : phosphorylation site (Y123)
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set_network_columns(network_columns)[source]¶ sets the network columns to use in analysis Parameters ———- network_columns : dict
required keys : substrate, site, kinase
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set_normalizers(normalizers)[source]¶ Sets the Kinase normalization values to be used in calculating the updated p-values Updated p-values normalized via original p-value / normalization-factor * normalization_factor
- Parameters
- normalizersdict or pandas df
Kinase Activity Normalizers key : str
Kinase Name
- valuefloat
Normalization Factor
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summarize_activites(activities=None, method='median_activity')[source]¶ Builds a single combined dataframe from the provided activites such that each piece of evidence is given a single column. Values are based on the method selected. The method must be a column in the activites
- Parameters
- activitiesdict
hypergeometric activities that have previously been summarized by network. key : experiment name value : hypergeometric activity
- methodstr
The column in the hypergeometric activity to use for summaring data
- Returns
- activity_summarypandas DataFrame
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The “Mapper” class¶
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class
kstar.mapper.experiment_mapper.ExperimentMapper(